Sensor and method

ABSTRACT

A sensor includes: a transmitting antenna with N transmitting antenna elements transmitting transmission signals; a receiving antenna with M receiving antenna elements, each receiving N received signals including a reflection signal generated by the living body reflecting part of the N transmission signals; a circuit; and a memory. The circuit extracts a second matrix corresponding to a specified frequency range from an N×M first matrix calculated from each received signal and indicating a propagation property between each transmitting antenna element and each receiving antenna element, estimates the position where the living body is present using the second matrix, calculates a radar cross-section (RCS) value of the living body based on the estimated position and the positions of the transmission and receiving antennas, and estimates the motion of the living body using the calculated RCS value and information indicating correspondence between RCS values and motions of the living body.

BACKGROUND 1. Technical Field

The present disclosure relates to a sensor and a method of estimatingactions of a living body using microwaves.

2. Description of the Related Art

As methods to identify the position and the action of a person, methodsusing microwaves have been studied (for example, see Japanese UnexaminedPatent Application Publication Nos. 2015-184149, 2006-81771,2001-159678, 2013-160730, 2004-340729, 2014-190724, and 2016-135233).Specifically, Japanese Unexamined Patent Application Publication No.2015-184149 discloses a method of judging whether a person is present bymonitoring the motion of the person using the change amount of areceived signal. Japanese Unexamined Patent Application Publication No.2006-81771 discloses a method of identifying the head and the limbs of aliving body, using THz waves. Japanese Unexamined Patent ApplicationPublication No. 2001-159678 discloses a method of estimating the size ofa target, using a radio wave radar. Japanese Unexamined PatentApplication Publication No. 2013-160730 discloses a method of measuringthe trace of a target, using a millimeter wave radar. JapaneseUnexamined Patent Application Publication No. 2004-340729 discloses amethod of judging whether a target is a person, by RCS measurement witha Doppler radar. Japanese Unexamined Patent Application Publication No.2014-190724 discloses a method of estimating the position and the stateof a living body by machine learning, using channel information ofmultiple antennas and various sensor information. Japanese UnexaminedPatent Application Publication No. 2016-135233 discloses a method ofdetermining which of a lying position and a sitting position a person istaking, based on the measurement result of an FMCW radar.

However, to improve the accuracy of estimating the motion of a livingbody using microwaves, further improvements are required.

SUMMARY

In one general aspect, the techniques disclosed here feature a sensorincluding: a transmitting antenna including N transmitting antennaelements, each of which transmits a transmission signal to a specifiedarea where a living body can be present, N being a natural number of 3or more; a receiving antenna including M receiving antenna elements,each of which receives N received signals including a reflection signalwhich the living body generates by reflecting part of the N transmissionsignals transmitted by the N transmitting antenna elements, M being anatural number of 3 or more; a circuit; and a memory which storescorrespondence information indicating correspondence of a motion of theliving body with temporal changes of a radar cross-section value and avertical position which is a position in a vertical direction at whichthe living body is present relative to the sensor, in which at leastthree transmitting antenna elements of the N transmitting antennaelements are arranged in positions different in the vertical directionand a horizontal direction, at least three receiving antenna elements ofthe M receiving antenna elements are arranged in positions different inthe vertical direction and a horizontal direction, and the circuitcalculates an N×M first matrix with components, each of which is acomplex transfer function indicating a propagation property between eachof the N transmitting antenna elements and each of the M receivingantenna elements, from each of the N received signals received by eachof the M receiving antenna elements during a specified period, extractsa second matrix corresponding to a specified frequency range in thefirst matrix, the second matrix corresponding to components affected bya vital activity including at least one of respiration, heartbeats, andbody motion of the living body, estimates a three-dimensional positionat which the living body is present relative to the sensor, using thesecond matrix, the three-dimensional position including the verticalposition, calculates a first distance indicating a distance between theliving body and the transmitting antenna and a second distanceindicating a distance between the living body and the receiving antenna,based on the estimated three-dimensional position, a position of thetransmitting antenna, and a position of the receiving antenna,calculates a radar cross-section value of the living body, using thefirst distance and the second distance, and estimates a motion of theliving body, using temporal changes of the estimated three-dimensionalposition and the calculated radar cross-section value, and thecorrespondence information stored in the memory.

The present disclosure makes it possible to estimate the motion of aliving body in a short time with high accuracy by using microwaves.

These general and specific aspects may be implemented using a system, amethod, and a computer program, and any combination of systems, methods,and computer programs.

Additional benefits and advantages of the disclosed embodiments willbecome apparent from the specification and drawings. The benefits and/oradvantages may be individually obtained by the various embodiments andfeatures of the specification and drawings, which need not all beprovided in order to obtain one or more of such benefits and/oradvantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of the configurationof a sensor according to an embodiment;

FIG. 2 is a block diagram illustrating the functional configurations ofa circuit and a memory according to the embodiment;

FIG. 3 is a diagram illustrating an installation example of the sensoraccording to the embodiment;

FIG. 4A is a diagram for explaining an example of position resolution ofa sensor measuring during different measurement periods;

FIG. 4B is a diagram explaining another example of position resolutionof a sensor measuring during different measurement periods;

FIG. 5 is a graph for explaining an example of extracting a motionperiod from time series data of a height position (Height) or an RCSvalue;

FIG. 6 is a diagram for explaining processing for direction vectorconversion;

FIG. 7 is a diagram illustrating an example of a direction code chart;

FIG. 8 is a graph illustrating an example of time series data ofcalculated direction codes and distances;

FIG. 9 illustrates a table illustrating an example of correspondenceinformation;

FIG. 10 is a graph illustrating test data obtained from measurement andmodel data which are model codes;

FIG. 11 is a flowchart illustrating an example of operation of thesensor according to the embodiment;

FIG. 12 is a flowchart illustrating an example of details of estimationprocessing; and

FIG. 13 is a flowchart illustrating an example of operation of thesensor in prior learning processing.

DETAILED DESCRIPTION (Underlying Knowledge Forming Basis of the PresentDisclosure)

The inventors studied in detail the related arts concerning stateestimation of a living body using microwaves. As a result, the inventorsfound that the method in Japanese Unexamined Patent ApplicationPublication No. 2015-184149 can detect whether a person is present, butthat the method has a problem that it is difficult to detect thedirection, position, state, motion, and the like of the person.

The inventors found that the method in Japanese Unexamined PatentApplication Publication No. 2006-81771 can detect the head and the limbsof a person and estimate the direction, position, and state and the likeof the person, but that the method has a problem that the cost is highbecause of the use of a terahertz band device.

The inventors found that the method in Japanese Unexamined PatentApplication Publication No. 2001-159678 can estimate the size of atarget, but that the method has a problem that it is difficult toestimate the state of a living body that is a person or the like.

The inventors found that the method in Japanese Unexamined PatentApplication Publication No. 2013-160730 can estimate the trace of theposition of a target living body, but that the method has a problem thatit is difficult to estimate the state of the living body.

The inventors found that the method in Japanese Unexamined PatentApplication Publication No. 2004-340729 can estimate whether a target isa person, using RCS, but that the method has a problem that it isdifficult to estimate the state of the living body that is a person orthe like.

The inventors found that Japanese Unexamined Patent ApplicationPublication No. 2014-190724 has a problem that machine learning isrequired for each user.

The inventors found that Japanese Unexamined Patent ApplicationPublication No. 2016-135233 has a problem that it is difficult toestimate the motion of a person.

As results of repeated research on the above problems, the inventorsfound out that it is possible to estimate the direction, position, size,posture, motion, and the like of a living body in a short time with highaccuracy, by using propagation properties and the Rader Cross Section ofreflection signals, which the living body generates by reflectingsignals transmitted from a transmitting antenna including multipleantenna elements arranged at positions different in the verticaldirection and horizontal direction. This finding led to the presentdisclosure.

To estimate the position of a living body at rest, data of severalseconds are necessary to estimate the motion of a living body becausethe position is estimated mainly based on components derived fromrespiration and heartbeats of the living body which occur in a cycle ofa few seconds. Meanwhile, in order to estimate a quick motion such asfalling down it was found that it is necessary to use data of a shorttime, less than 1 second, because the quick motion is estimated based oncomponents derived from body motion of the living body with a durationof less than 1 second, and in order to estimate the posture of a livingbody at rest, the posture of a living body in motion, and the posture ofa living body both at rest and in motion, it was found that it isnecessary to use different time data lengths for data of a singlemeasurement to estimate the position and the posture of the living body.

(1) A sensor according an aspect of the present disclosure is a sensorincluding: a transmitting antenna including N transmitting antennaelements, each of which transmits a transmission signal to a specifiedarea where a living body can be present, N being a natural number of 3or more; a receiving antenna including M receiving antenna elements,each of which receives N received signals including a reflection signalwhich the living body generates by reflecting part of the N transmissionsignals transmitted by the N transmitting antenna elements, M being anatural number of 3 or more; a circuit; and a memory which storescorrespondence information indicating correspondence of a motion of theliving body with temporal changes of a radar cross-section value and avertical position which is a position in a vertical direction at whichthe living body is present relative to the sensor, in which at leastthree transmitting antenna elements of the N transmitting antennaelements are arranged in positions different in the vertical directionand a horizontal direction, at least three receiving antenna elements ofthe M receiving antenna elements are arranged in positions different inthe vertical direction and a horizontal direction, and the circuitcalculates an N×M first matrix with components, each of which is acomplex transfer function indicating a propagation property between eachof the N transmitting antenna elements and each of the M receivingantenna elements, from each of the N received signals received by eachof the M receiving antenna elements during a specified period, extractsa second matrix corresponding to a specified frequency range in thefirst matrix, the second matrix corresponding to components affected bya vital activity including at least one of respiration, heartbeats, andbody motion of the living body, estimates a three-dimensional positionat which the living body is present relative to the sensor, using thesecond matrix, the three-dimensional position including the verticalposition, calculates a first distance indicating a distance between theliving body and the transmitting antenna and a second distanceindicating a distance between the living body and the receiving antenna,based on the estimated three-dimensional position, a position of thetransmitting antenna, and a position of the receiving antenna,calculates a radar cross-section value of the living body, using thefirst distance and the second distance, and estimates a motion of theliving body, using temporal changes of the estimated three-dimensionalposition and the calculated radar cross-section value, and thecorrespondence information stored in the memory.

This configuration makes it possible to estimate the motion of a livingbody in a short time with high accuracy using microwaves.

(2) In the above aspect, the motion of the living body associated in thecorrespondence information may include falling down, sitting on a chair,sitting on a floor, standing up from a chair, standing up from a floor,jumping, and turning direction, and the circuit may estimate whichmotion the living body is performing, out of the falling down, sittingon a chair, sitting on a floor, standing up from a chair, standing upfrom a floor, jumping, and turning direction, using the temporal changesof the estimated three-dimensional position and the calculated radarcross-section value and the correspondence information stored in thememory.

This configuration makes it possible to estimate the motion of a livingbody in a shorter time.

(3) In the above aspect, in estimating the motion of the living body,the circuit may extract a period in which the temporal change of theestimated vertical position or the calculated radar cross-section valueis larger than a predetermined value, as a motion period in which theliving body is in motion, and may estimate the motion of the livingbody, using the temporal changes of the estimated three-dimensionalposition and the calculated radar cross-section value during theextracted motion period, and the correspondence information stored inthe memory.

This configuration makes it possible to reduce a processing loadinvolved in estimating the motion of a living body.

(4) In the above aspect, the circuit may extract the motion period,using time series data obtained from a plurality of the verticalpositions or a plurality of the radar cross-section values obtained intime series by removing an instantaneous noise component using apredetermined filter.

This configuration makes it possible to estimate the motion of a livingbody with higher accuracy.

(5) In the above aspect, the vertical position and the radarcross-section value which are associated with the motion of the livingbody in the correspondence information may be expressed by a directioncode which is obtained by normalizing a direction vector into which thetemporal changes of the vertical position estimated by the circuit andthe radar cross-section value calculated by the circuit in advance whenthe living body performs one motion as the motion of the living body inthe specified area are converted by using a predetermined method, and inestimating the motion of the living body, the circuit may convert thetemporal changes of the vertical position obtained from the estimatedthree-dimensional position and the calculated radar cross-section valueduring the extracted motion period into a direction vector using apredetermined method, calculate a direction code by normalizing thedirection vector obtained from conversion, and estimate the motion ofthe living body using the calculated direction code and thecorresponding information.

This configuration makes it possible to estimate the motion of a livingbody with higher accuracy.

(6) In the above aspect, the circuit may estimate the motion of theliving body during a second motion period next to a first motion period,using a posture of the living body at the end of the first motionperiod.

This makes it possible to estimate the next motion of a living body,utilizing an estimated motion of the living body. This allows forefficient estimation of the motion of a living body.

(7) In the above aspect, when a variation in a horizontal direction ofthe estimated three-dimensional position is larger than or equal to apredetermined distance, the circuit may further estimate that the livingbody is moving in the horizontal direction.

This configuration makes it possible to estimate the movement of aliving body in the horizontal direction in a short time with highaccuracy.

(8) In the above aspect, the circuit may further estimate a height ofthe living body, using the vertical position included in thethree-dimensional position from which the living body is estimated to bemoving in the horizontal direction.

This configuration makes it possible to estimate the height of a livingbody in a short time with high accuracy. Hence, for example, if multipleliving bodies which can be present in a specified area are known inadvance, and the heights of the multiple living bodies are different,this method can be utilized to identify which living body of themultiple living bodies is present and in motion.

(9) In the above aspect, the circuit may further estimate a body size ofthe living body, using the radar cross-section value calculated when theliving body is estimated to be moving in the horizontal direction.

This configuration makes it possible to estimate the body size of aliving body in a short time with high accuracy. Hence, for example, ifmultiple living bodies which can be present in a specified area areknown in advance, and the body sizes of the multiple living bodies aredifferent, this method can be utilized to identify which living body ofthe multiple living bodies is present and in motion.

(10) In the above aspect, the specified period may be about half a cycleof at least one of respiration, heartbeats, and body motion of theliving body.

This makes it possible to estimate the motion of a living bodyefficiently.

Note that not only may the present disclosure be implemented as asensor, but the present disclosure may be implemented as an integratedcircuit device including processing means included in such a sensor; asa method having the processing means included in the device, as steps;as a program executed by a computer and including these steps; and asinformation, data, or signals indicating the program. These program,information, data, and signals may be distributed via a recording mediumsuch as a CD-ROM or a communication medium such as the Internet.

Hereinafter, an embodiment of the present disclosure will be describedin detail using the drawings. Note that each of the embodimentsdescribed below is for illustrating a specific preferred example.Values, shapes, materials, constituents, arrangements and connectionmethods of constituents, steps, the orders of steps, and the like shownin the embodiment below are mere examples and are not intended to limitthe present disclosure. In addition, of the constituents in thefollowing embodiments, the constituents which are not recited inindependent claims showing the highest level concept are described asoptional constituents to constitute more preferred embodiments. Notethat in this specification and the drawings, constituents havingsubstantially the same functional configurations are denoted by the samesymbols, and repetitive descriptions thereof will be omitted.

Embodiment

FIG. 1 is a block diagram illustrating an example of the configurationof a sensor according to an embodiment. FIG. 3 is a diagram illustratingan installation example of the sensor according to the embodiment.

As illustrated in FIG. 1, a sensor 10 includes a transmitting antenna20, a receiving antenna 30, circuit 40, and memory 41. The sensor 10emits microwaves to a living body 50, such as a human, from thetransmitting antenna 20 and receives the reflected waves reflected fromthe living body 50 with the receiving antenna 30. Here, φ_(T) is anangle formed between a first reference direction which is a direction onthe horizontal plane and arbitrarily set relative to the transmittingantenna 20, and a first living body direction which is a direction fromthe transmitting antenna 20 toward the living body 50. θ_(T) is anelevation angle of the living body 50 formed between the verticaldirection and the first living body direction. φ_(R) is an elevationangle of the living body 50 formed between a second reference directionwhich is a direction on the horizontal plane and arbitrarily setrelative to the receiving antenna 30, and a second living body directionwhich is a direction from the receiving antenna 30 toward the livingbody 50. θ_(R) is an angle formed between the vertical direction and thesecond living body direction. When (x_(b), y_(b), z_(b)) is the centercoordinates of a part at which the living body 50 performs vitalactivities, the directions (θ_(T), θ_(R), φ_(T), φ_(R)) and thecoordinates (x_(b), y_(b), z_(b)) can be mutually converted according tothe positional relationship between the transmitting antenna 20, thereceiving antenna 30, and the living body 50.

The transmitting antenna 20 includes N transmitting antenna elements 21.The transmitting antenna 20 has an array antenna in which N_(x)transmitting antenna elements 21 line in the horizontal direction (xdirection) and N_(z) transmitting antenna elements 21 in the verticaldirection (z direction) so that N (N_(x)×N_(z)) transmitting antennaelements 21 are arranged in a rectangular shape. In other words, atleast three transmitting antenna elements 21 of the N transmittingantenna elements 21 are arranged at positions different in the verticaldirection and the horizontal direction. Each of the N transmittingantenna elements 21 transmits a transmission signal to a specified areawhere a living body can be present. In other words, the transmittingantenna 20 transmits N transmission signals to the specified area from Ndifferent positions. Note that the specified area where a living bodycan be present means a detection area where the sensor 10 detects theexistence of a living body.

Specifically, Each of the N transmitting antenna elements 21 emitsmicrowaves as a transmission signal to the living body 50, such as ahuman. The N transmitting antenna elements 21 may transmit astransmission signals, signals subjected to different modulationprocessing for each antenna element 21. Each of the N transmittingantenna elements 21 may sequentially switch a modulated signal and anunmodulated signal to transmit. The modulation processing may beperformed by the transmitting antenna 20. By making the transmissionsignals transmitted from the N transmitting antenna elements 21different from each other for each of the N transmitting antennaelements 21 as described above, it is possible to identify from atransmission signal received by the receiving antenna 30, thetransmitting antenna element 21 which transmitted the transmissionsignal. As above, the transmitting antenna 20 may include a circuit forperforming modulation processing.

The receiving antenna 30 includes M receiving antenna elements 31. Thereceiving antenna 30 has an array antenna in which M_(x) receivingantenna elements 31 line in the horizontal direction (x direction) andM_(z) receiving antenna elements 31 in the vertical direction (zdirection) so that M (M_(x)×M_(z)) receiving antenna elements 31 arearranged in a rectangular shape. In other words, at least threereceiving antenna elements 31 of the M receiving antenna elements 31 arearranged at positions different in the vertical direction and thehorizontal direction. Each of the M receiving antenna elements 31receives N received signals including reflection signals which aresignals reflected by the living body 50 out of the N transmissionsignals. The receiving antenna 30 frequency-converts the receivedsignals, which are microwaves, into low frequency signals. The receivingantenna 30 outputs to the circuit 40 the signals obtained from theconversion into the low frequency signals. In other words, the receivingantenna 30 may include a circuit for processing the received signals.

The circuit 40 performs various processing to operate the sensor 10. Thecircuit 40, for example, includes a processor which executes a controlprogram and a volatile storage area (main storage apparatus) used as awork area when the control program is executed. The volatile storagearea is, for example, a random access memory (RAM). Note that thecircuit 40 may be constituted of a dedicated circuit for performingvarious processing to operate the sensor 10. In other words, the circuit40 may be a circuit performing software processing or a circuitperforming hardware processing.

The memory 41 is a nonvolatile storage area (auxiliary storageapparatus), such as a read only memory (ROM), flash memory, or hard diskdrive (HDD). The memory 41 stores, for example, information utilized forvarious processing to operate the sensor 10.

Next, the functional configuration of the circuit 40 will be describedusing FIG. 2.

FIG. 2 is a block diagram illustrating the functional configuration ofthe circuit and the memory according to the embodiment.

The circuit 40 includes a complex transfer function calculator 410, aliving body component calculator 420, a position estimation processor430, an RCS calculator 440, and a motion estimator 450.

The complex transfer function calculator 410 calculates a complextransfer function from the low frequency signal into which the receivedsignal has been converted. The complex transfer function indicatespropagation loss and phase rotation between each transmitting antennaelement 21 and each receiving antenna element 31. In the case where thenumber of the transmitting antenna elements is N and the number of thereceiving antenna elements is M, the complex transfer function is acomplex matrix with M×N components. Hereinafter, this complex matrix isreferred to as a complex transfer function matrix. The estimated complextransfer function matrix is outputted to the living body componentcalculator 420. In other words, the complex transfer function calculator410 calculates an N×M first matrix with the components, each of which isa complex transfer function indicating a propagation property betweeneach of the N transmitting antenna elements 21 and each of the Mreceiving antenna elements 31, from each of the multiple receivedsignals received by all the M receiving antenna elements 31 during aspecified period.

The living body component calculator 420 separates the components intocomplex transfer function matrix components obtained from the receivedsignals received via the living body 50 and complex transfer functionmatrix components obtained from the receptions signals received not viathe living body 50. The components via the living body 50 meanscomponents which vary with time due to the biological activities. Hence,assuming, for example, that things other than the living body 50 remainstationary, the components via the living body 50 can be extracted bytaking out components other than direct current from the componentsobtained by Fourier transforming the components of the complex transferfunction matrix in the time direction. Alternatively, the components viathe living body 50 can also be extracted, for example, by taking out thecomponents of which the differences from the results measured when theliving body 50 was not present in the specified area exceed apredetermined threshold. In this way, the living body componentcalculator 420 calculates the extracted complex transfer function matrixcomponents as living body components, by extracting the complex transferfunction matrix components obtained from the received signals includingthe reflection signals via the living body 50. In other words, theliving body component calculator 420 extracts a second matrixcorresponding to a specified frequency range in the first matrix, thesecond matrix corresponding to the components affected by vitalactivities including at least one of respiration, heartbeats, and bodymotion of the living body. The specified frequency range, for example,includes frequencies derived from the vital activities described aboveincluding at least one of respiration, heartbeats, and body motion ofthe living body. The specified frequency range is, for example, from 0.1to 3 Hz inclusive. This frequency range makes it possible to extractliving body components affected by vital activities at a part of livingbody 50 by the movement of the heart, lungs, diaphragm or other internalorgans, or vital activities of the hands and the legs. Note that a partof living body 50 by the movement of the heart, lungs, diaphragm, orother internal organs is, for example, the pit of the stomach of ahuman.

Here, the living body components are a matrix with M×N components, whichare extracted from the complex transfer functions obtained from thereceived signals measured by the receiving antenna 30 during thespecified period. For this reason, it is assumed that the living bodycomponents include a frequency response or time response information.Note that the specified period is about half the cycle of at least oneof respiration, heartbeats, and body motion of the living body.

The living body components calculated by the living body componentcalculator 420 are outputted to the position estimation processor 430.The position estimation processor 430 estimates the position of theliving body using the calculated living body components. In other words,using the second matrix, the position estimation processor 430 estimatesthe three-dimensional position where the living body 50 is presentrelative to the sensor 10, the three-dimensional position including thevertical position of the living body relative to the sensor 10. Toestimate the position estimation, both departure angle θ_(T) from thetransmitting antenna 20 and arrival angle θ_(R) to the receiving antenna30 are estimated, and then the position of the living body 50 isestimated by triangulation using the estimated departure angle θ_(T) andarrival angle θ_(R).

Note that as for the position estimation, in the case where a firstdistance between the living body 50 to be measured and the transmittingantenna 20 or a second distance between the living body 50 and thereceiving antenna 30 is so small as to be the same as the aperture ofthe array antenna included in the transmitting antenna 20 or thereceiving antenna 30, it is possible to use what is called sphericalmode vectors to estimate the position of the living body 50. This isbecause the departure angles from and the arrival angles to the arrayantenna elements are different for each array antenna element includedin the transmitting antenna 20 or the receiving antenna 30. Since it isimpossible in this case to define a departure angle and an arrival angleunlike the plane waves, steering vectors described later are definedusing two dimensional or three-dimensional coordinates indicating thepositional relationships between the target living body 50 and the arrayantenna elements included in the transmitting antenna 20 or thereceiving antenna 30.

Note that depending on the purpose, the position estimation processor430 may estimate the position of the living body 50 at rest frommeasurement data obtained by measuring during a first measurement periodwhich is, for example, 3 seconds or more, and in addition also estimatethe position of the living body 50 in motion using measurement dataobtained by measuring during a second measurement period which isshorter than the first measurement period, such as 1 second, or 0.5seconds, for example. The concept of this case is illustrated in FIGS.4A and 4B.

FIG. 4A is a diagram for explaining an example of position resolution ofa sensor measuring during different measurement periods.

As illustrated in FIG. 4A, for example, in the case where the entirearea of a lattice 301 including multiple quadrangle areas, for example,indicates a specified area which is the measurement area of the sensor10, and the area is a space of 8 m×8 m, the area shows multiple areaswhich are measured during the first measurement period at a resolutionof 100 cm square. In addition, in the case where the entire area of alattice 302 including multiple quadrangle areas indicates a specifiedarea which is the measurement area of the sensor 10, and the area is aspace of 8 m×8 m which is the same as the lattice 301, the area showsmultiple areas which are measured during the second measurement periodat a resolution of 5 cm square. As described above, the firstmeasurement period is, for example, 3 seconds, and the secondmeasurement period is, for example, 0.5 seconds. A first period taken tomeasure each of the multiple areas partitioned by the lattice 301 duringthe first measurement period and a second period taken to measure eachof the multiple areas partitioned by the lattice 302 during for thesecond measurement period overlap each other. In other words, processingto measure each of the multiple areas partitioned by the lattice 301 andprocessing to measure each of the multiple areas partitioned by thelattice 302 are executed by parallel processing.

Processing to measure the living body 50 during different measurementperiods may be executed as follows.

FIG. 4B is a diagram for explaining another example of positionresolution of a sensor measuring during different measurement periods.

As illustrated in FIG. 4B, for example, in the case where the entirearea of the lattice 301 including the multiple quadrangle areasindicates a specified area which is the measurement area of the sensor10 and is a space of 8 m×8 m in the same way as in the explanation forFIG. 4A, the area shows multiple areas which are measured during thefirst measurement period at a resolution of 100 cm square. The entirearea of a lattice 303 including multiple quadrangle areas shows multipleareas which are measured during the second measurement period at aresolution of 30 cm square in a space of 4 m×4 m which is the areaincluding the position of the living body 50 detected by the measurementusing the multiple areas of the lattice 301. In addition, the entirearea of a lattice 304 including multiple quadrangle areas shows multipleareas which are measured during a third measurement period shorter thanthe second measurement period at a resolution of 10 cm square in a spaceof 2 m×2 m which is the area including the position of the living body50 detected by the measurement using the multiple areas of the lattice303. The first measurement period is, for example, 3 seconds, the secondmeasurement period is, for example, 1 second, and the third measurementperiod is, for example, 0.5 seconds. Note that to improve the detectionaccuracy of the position of the living body 50 at rest, the firstmeasurement period may be set to a time longer than 3 seconds, such as10 to 20 seconds.

The RCS calculator 440 calculates the Rader Cross Section (RCS: RadarCross Section) using the living body components and the estimatedposition. Specifically, to calculate the scattering cross section, theRCS calculator 440 calculates distance RT indicating the first distancebetween the living body 50 and the transmitting antenna 20 and distanceRR indicating the second distance between the living body 50 and thereceiving antenna 30 based on the estimated three-dimensional position,the position of the transmitting antenna 20, and the position of thereceiving antenna 30. The RCS calculator 440 calculates a propagationdistance from the calculated distance RT and distance RR and calculatesthe RCS using the calculated propagation distance and the intensity ofthe living body components. Note that the positions of the transmittingantenna 20 and the receiving antenna 30 may be stored in advance in thememory 41.

The motion estimator 450 estimates the motion of the living body 50using time series data indicating temporal changes of thethree-dimensional position estimated by the position estimationprocessor 430 and the RCS value calculated by the RCS calculator 440,and correspondence information 42 stored in advance in the memory 41.The motion estimator 450 includes a motion period extractor 451, adirection code calculator 452, and a motion comparator 453.

As illustrated in FIG. 5, the motion period extractor 451 extracts as amotion period, a period in which the amount of the temporal change ofthe three-dimensional position of the living body 50 estimated by theposition estimation processor 430 or the amount of the temporal changeof the RCS value calculated by the RCS calculator 440 is larger than apredetermined value. Note that FIG. 5 is a graph for explaining anexample of extracting a motion period from time series data of theheight position (Height) or the RCS value.

When extracting a motion period using the height position, which is thevertical position, or the RCS value, for example, the motion periodextractor 451 may use, in order to avoid the influence of instantaneousnoises, for example, a median filter, an FIR filter, or average valuesfor the time series data of the obtained three-dimensional position orRCS value to remove noise components in the height position and the RCSvalue, and may extract as a motion period of the living body, a changingsection of the height information or a RCS changing section afterfiltering. In other words, the motion period extractor 451 may extract amotion period using time series data obtained by removing instantaneousnoise components using a predetermined filter from multiple verticalpositions or multiple RCS values obtained in time series. FIG. 5illustrates, for example, a state where the height position and the RCSvalue were measured using measurement data of a measurement period ofabout 0.6 seconds and subjected to a predetermined filtering processing,and a state of extracting a motion period. Note that although the motionperiod extractor 451 is effective to limit a period to be used for theestimation for the purpose of reducing the calculation amount or thelike, the motion period extractor 451 is not always necessary. In otherwords, it goes without saying that in the case of estimating the statefor the entire section, the motion period extractor 451 may beeliminated and the entire section may be used for the estimation.

The direction code calculator 452 converts the temporal changes of thevertical position (height position) obtained from the estimatedthree-dimensional position and the calculated RCS value during themotion period extracted by the motion period extractor 451, into adirection vector using a predetermined method. Specifically, thedirection code calculator 452 two-dimensionally plots height positionsand RCS values as illustrated in FIG. 6 and calculates distance ΔP anddirection θ of the trace indicating the temporal change of the plottedpoints. For example, the direction code calculator 452 performsdirection vector conversion by calculating distance ΔP between firstcoordinates p1(H1,R1) and second coordinates p2(H2,R2) and direction θof second coordinates p2(H2,R2) viewed from first coordinates p1(H1,R1)from the trace from first coordinates p1(H1,R1) indicated by heightposition H1 and RCS value R1 at a first timing to second coordinatesp2(H2,R2) indicated by height position H2 and RCS value R2 at a secondtiming which is the next timing to the first timing. FIG. 6 is a diagramfor explaining the processing for the direction vector conversion.

Next, the direction code calculator 452 normalizes the converteddirection vector to calculate a direction code. Specifically, thedirection code calculator 452 calculates a direction code by referringto a direction code chart illustrated in FIG. 7. For example, thedirection code calculator 452 identifies the direction code closest todirection θ out of the direction codes indicated by 1 to 8. FIG. 7 is adiagram illustrating an example of the direction code chart.

The direction code calculator 452 obtains time series data of directioncodes as illustrated in FIG. 8 by calculating direction codes anddistances ΔP as described above. FIG. 8 is a graph illustrating anexample of time series data of calculated direction codes and distances.Note that at this time, the direction code calculator 452 may normalizedirection codes to avoid the influence of individual difference.

The motion comparator 453 compares the time series data of directioncodes calculated by the direction code calculator 452 with thecorrespondence information 42 stored in the memory to identify a motionassociated with the time series data in the correspondence information42, and thus estimates a motion of the living body 50.

Note that the correspondence information 42 stored in the memory 41 isinformation indicating the correspondence between a motion of the livingbody 50 and multiple model codes indicating the temporal changes of theRCS value and the vertical position which is a position of the livingbody 50 in the vertical direction relative to the sensor 10. The motionsof the living body 50 associated in the correspondence information 42include falling down, sitting on a chair, sitting on a floor, standingup from a chair, standing up from a floor, jumping, and turningdirection, as illustrated in FIG. 9. In other words, the motionestimator 450 estimates which one of the motions, falling down, sittingon a chair, sitting on a floor, standing up from a chair, standing upfrom a floor, jumping, and turning direction, the living body 50 did,using temporal changes of the three-dimensional position estimated bythe position estimation processor 430 and the RCS value calculated bythe RCS calculator 440, and the correspondence information 42 stored inadvance in the memory 41. Note that the model code is expressed as timeseries data as illustrated in FIG. 10.

Note that the circuit 40 repeatedly performs the foregoing processing atthe sections 410 to 450 at multiple different timings to obtain the timeseries data. For example, the circuit 40 repeatedly performs processingat a predetermined sampling frequency as described using FIG. 4A or FIG.4B to obtain time series data including multiple three-dimensionalpositions in time series and multiple RCS values in time series.

Next, the operation principle of the sensor 10 of the embodiment will bedescribed in detail using mathematical formulae. Note that heredescribed is a method of extracting living body components using Fouriertransformation. The processing described here is performed by thecircuit 40. In the case where L people are present at an indoorenvironment, the transmitting antenna 20 is a planar array antennaincluding M_(T) elements, and the receiving antenna 30 is a planar arrayantenna including M_(R) elements, the measured M_(R)×M_(T) time varyingMIMO channel H(t) is expressed as the following Formula 1.

$\begin{matrix}{{H(t)} = \begin{pmatrix}{h_{11}(t)} & \ldots & {h_{2M_{T}}(t)} \\\vdots & \ddots & \vdots \\{h_{M_{R}1}(t)} & \ldots & {h_{M_{R}M_{T}}(t)}\end{pmatrix}} & \left( {{Formula}\mspace{14mu} 1} \right)\end{matrix}$

Here, t is a measurement time, and h_(ij) which is a (i,j)-th element isthe channel response from a j-th transmitting antenna element 21 to ani-th receiving antenna element 31.

The M_(R)×M_(T) MIMO array can be converted into a MIMO virtual arrayexpressed in M_(R)M_(T)×1 SIMO (Single-Input Multiple-Output)configuration. Here, M_(R)×M_(T) MIMO channel h(t) is converted into anM_(R)M_(T)×1 virtual SIMO channel which is expressed by the followingFormula 2.

h(t)=[h ₁₁(t), . . . ,h _(M) _(R) ₁(t),h ₁₂(t), . . . ,h _(M) _(R) ₂(t),. . . ,h _(1M) _(T) (t), . . . ,h _(M) _(R) _(M) _(T) (t)]^(T)  (Formula2)

Here, {⋅}^(T) indicates transposition. Here, using time difference T,difference channel h_(sb)(t,T) is defined as the following Formula 3.

h _(sb)(t,T)=h(t+T)−h(t)  (Formula 3)

Although an actual complex channel includes reflection waves not via theliving body, such as direct waves and reflection waves derived fromstationary objects, all the reflection waves not via the living body arecancelled in a difference channel matrix by subtraction operation. Thus,the complex channel includes only reflection waves derived from theliving body.

Here, using difference channel h_(sb)(t,T), instantaneous correlationmatrix R(t,T) of difference time T at a measurement time t is defined asthe following Formula 4.

R(t,T)=h _(sb)(t,T)h _(sb)(t,T)^(H)  (Formula 4)

Here, {⋅}_(H) indicates complex conjugate transposition. Although therank of this instantaneous correlation matrix is 1, it is possible toincrease the rank of the correlation matrix by average operation, andthis makes it possible to perform simultaneous estimation on multipleincoming waves.

Next, descriptions will be provided for a method of three-dimensionaldirection estimation of the living body, using the correlation matrixobtained from the difference channel matrix. Here, an estimation methodbased on MUSIC algorithm will be described. The above correlation matrixR is expressed as the following Formulae 5 to 7 by eigenvaluedecomposition.

R=U∧U ^(H)  (Formula 5)

U=[u ₁ , . . . ,u _(L) ,u _(L+1) , . . . ,u _(M) _(R) _(M) _(T)]  (Formula 6)

∧=diag[λ₁, . . . ,λ_(L),λ_(L+1), . . . ,λ_(M) _(R) _(M) _(T) ]  (Formula7)

Here, U is an eigenvector, ∧ is an eigenvalue corresponding to theeigenvector, which is in the order indicated by the following Formula 8.

λ₁≥λ₂≥ . . . ≥λ_(L)≥λ_(L+1)≥ . . . ≥λ_(M) _(R) _(M) _(T)   (Formula 8)

L is the number of incoming waves, in other words, the number of livingbody to be detected. In the following description, it is assumed thatthe distance to the living body to be detected is relatively smallcompared to the aperture of the array antenna included in thetransmitting antenna 20 or the receiving antenna 30. Hence, it isassumed that the array antenna receives spherical waves. Note that evenif the distance to the living body to be detected is sufficiently far,the following formulae hold true, and thus there is no problem indetection. In the case where it is known that the distance to the targetis sufficiently far, the position of the target may be estimated usingdeparture angle θ_(T) and arrival angle θ_(R), which would provide anadvantage that the calculation is relatively simple. The steering vectorof the array antenna on the transmitting antenna 20 side is defined asthe following Formulae 9 to 11.

$\begin{matrix}{{\alpha_{T}\left( {x,y,z} \right)} = \left\lbrack {{\exp \left( {{- j}\; \Phi_{11}} \right)},\ldots,{\exp \left( {{- j}\; \Phi_{n_{x}1}} \right)},{\exp \left( {{- j}\; \Phi_{12}} \right)},\ldots,{\exp \left( {{- j}\; \Phi_{n_{x}2}} \right)},\ldots,{\exp \left( {{- j}\; \Phi_{n_{x}n_{y}}} \right)}} \right\rbrack^{T}} & \left( {{Formula}\mspace{14mu} 9} \right) \\{\mspace{76mu} {\Phi_{n_{x}n_{y}} = \frac{2{\pi \left( {d_{n_{xc}n_{yc}} - d_{n_{x}n_{y}}} \right)}}{\lambda}}} & \left( {{Formula}\mspace{14mu} 10} \right) \\{\mspace{76mu} {d_{n_{x}n_{y}} = \sqrt{\left( {x - {dx}_{n_{x}}} \right)^{2} + \left( {y - {dy}_{n_{y}}} \right)^{2} + \left( {z - {dz}_{n_{z}}} \right)^{2}}}} & \left( {{Formula}\mspace{14mu} 11} \right)\end{matrix}$

Similarly, the steering vector of the array antenna on the receivingantenna 30 side is defined as the following Formulae 12 to 14.

$\begin{matrix}{{\alpha_{R}\left( {x,y,z} \right)} = \left\lbrack {{\exp \left( {{- j}\; \Theta_{11}} \right)},\ldots,{\exp \left( {{- j}\; \Theta_{m_{x}1}} \right)},{\exp \left( {{- j}\; \Theta_{12}} \right)},\ldots,{\exp \left( {{- j}\; \Theta_{m_{x}2}} \right)},\ldots,{\exp \left( {{- j}\; \Theta_{m_{x}m_{y}}} \right)}} \right\rbrack^{T}} & \left( {{Formula}\mspace{14mu} 12} \right) \\{\mspace{76mu} {\Theta_{m_{x}m_{y}} = \frac{2{\pi \left( {d_{m_{xc}m_{yc}} - d_{m_{x}m_{y}}} \right)}}{\lambda}}} & \left( {{Formula}\mspace{14mu} 13} \right) \\{\mspace{76mu} {d_{m_{x}m_{y}} = \sqrt{\left( {x - {dx}_{m_{x}}} \right)^{2} + \left( {y - {dy}_{m_{y}}} \right)^{2} + \left( {z - {dz}_{m_{z}}} \right)^{2}}}} & \left( {{Formula}\mspace{14mu} 14} \right)\end{matrix}$

Here, d_(mxmy) and d_(nxny) are the distances between a wave source, inother words, the living body, and m_(x)m_(y)-th and n_(x)n_(y)-th arrayelements, respectively; d_(mxcmyc) and d_(nxcnyc) are the distancesbetween the wave source and reference elements; Θ_(nxny), Φ_(nxny) arethe phase delays; and λ is the wavelength.

Further, by multiplying the steering vectors of transmission andreception together, a steering vector considering angle information ofboth transmission and reception is defined as illustrated in thefollowing Formula 15.

α(x,y,z)=vec(α_(R)(x,y,z)α_(T) ^(T)(x,y,z))  (Formula 15)

Applying MUSIC method to this, the direction of an incoming wave isestimated by searching for a local maximum value of the evaluationfunction expressed by the following Formula 16, using this steeringvector.

$\begin{matrix}{{P_{music}\left( {x,y,z} \right)} = \frac{1}{\left| {{a^{H}\left( {x,y,z} \right)}\left\lbrack {u_{L + 1},\ldots,w_{M_{R}M_{T}}} \right\rbrack} \right|^{2}}} & \left( {{Formula}\mspace{14mu} 16} \right)\end{matrix}$

Here, three dimensional search processing is executed because thissearch is performed on coordinates (x,y,z) of a space.

Further, the Rader Cross Section (RCS) from the living body iscalculated using position information (x,y) obtained from the search.

Using frequency response matrix F(ω) obtained by Fourier transformingmeasured propagation channel matrix H(t) and vectorizing the resultant,reception electric power P_(γ)(ω) is expressed as the following Formula17.

P _(γ)(ω)=F(ω)F(ω)^(H)  (Formula 17)

Here, scattering cross section σ of the living body is expressed as thefollowing Formulae 18 to 20.

$\begin{matrix}{\sigma = {\frac{\left( {4\pi} \right)^{2}R_{R}^{2}R_{T}^{2}}{G_{R}G_{T}\lambda^{2}}\frac{\int_{\omega_{1}}^{\omega_{2}}{{P_{T}(\omega)}d\; \omega}}{P_{t}}}} & \left( {{Formula}\mspace{14mu} 18} \right) \\{R_{R} = \sqrt{\left( {x - x_{R}} \right)^{2} + \left( {y - y_{R}} \right)^{2}}} & \left( {{Formula}\mspace{14mu} 19} \right) \\{R_{T} = \sqrt{\left( {x - x_{T}} \right)^{2} + \left( {y - y_{T}} \right)^{2}}} & \left( {{Formula}\mspace{14mu} 20} \right)\end{matrix}$

Here, R_(R) is the distance from the receiving antenna 30 to theestimated position of the living body 50, and R_(T) is the distance fromthe transmitting antenna 20 to the estimated position of the living body50. G_(R) is the gain of the receiving antenna 30, and G_(T) is the gainof the transmitting antenna 20. In addition, ω₁ of is the minimumfrequency of biological activities, and ω₂ is the maximum frequency ofthe biological activities.

As described above, by extracting the only components of the frequenciescorresponding to biological activities, it is possible to extract onlyelectric power reflected by the living body. The body surface areaviewed from the antenna is different depending on the posture of theliving body, so that it is possible to estimate the state of the livingbody. In addition to it, the action of the living body increases thefrequency components caused by the body motion, resulting in thevariation of RCS, so that by modeling the trace of the estimated heightz and Rader Cross Section σ of the living body, it is possible toestimate the motion of the living body.

A method of motion estimation will be described below.

With the method described above, it is possible to measure the trace ofσ-z characteristic by estimating scattering cross section σ and height zof the living body continuously at multiple different timings. Here,since when the living body is at rest, there is almost no change in theRCS value, a flow of trace points in which the living body is in motion,in other words, in which the variation of the RCS value is large isextracted. As for the group of trace points, when ΔPi is a trace pointmovement amount which is the distance between an (i−1)-th trace pointand an i-th trace point, and αi is an angle parameter which is the angleformed between the (i−1)-th trace point and the i-th trace point, ΔPiand αi are defined by the following Formulae 21 and 22, respectively.

$\begin{matrix}{{\Delta \; P_{i}} = \sqrt{\left( {\sigma_{i} - \sigma_{i - 1}} \right)^{2} + \left( {z_{i} - z_{i - 1}} \right)^{2}}} & \left( {{Formula}\mspace{14mu} 21} \right) \\{\alpha_{i} = {\tan^{- 1}\left( \frac{z_{i} - z_{i - 1}}{\sigma_{i} - \sigma_{i - 1}} \right)}} & \left( {{Formula}\mspace{14mu} 22} \right)\end{matrix}$

Next, using the value of the angle parameter, a direction code isassigned to the moving direction of a trace point. To assign a directioncode to an angle, 360° is divided into 8 angles, to each of which thenumber of 1 to 8 is assigned. In this case, to prevent the code fromchanging very often for movement in the vertical direction and thehorizontal direction, it is also possible not to set the boundary ofcodes in these directions. Since there are individual differences in thespeed of movement, normalization of direction codes may be performedconsidering the difference in the speed of motion to avoid erroneousrecognition caused because the number of trace points differs due to thespeed of motion even though the motion is the same. For example, fromoriginal direction code string c(j=1 to j_(max)) obtained by thedirection estimation, a normalized code string having K terms isgenerated considering the ratio of the trace point movement amount tothe sum of the trace point movement amounts. Here, j_(max) is the numberof trace points, which is different depending on the motion time.Normalized code string C_(k)(k=1, 2, . . . , K) is generated from i-thtrace movement amount ΔPi and the sum of trace point movement amountsΔP_(sum) as follows.

1) When j=1, the normalized code string in which k is within the rangesatisfying the following Formula 23 is the original direction codestring with j=1.

$\begin{matrix}{1 \leq k \leq {\frac{\Delta \; P_{1}}{\Delta \; P_{sum}} \times K}} & \left( {{Formula}\mspace{14mu} 23} \right)\end{matrix}$

2) when j is within the range of 2 to j_(max), the normalized codestring in which k is within the range satisfying the following formula24 is the code with each j. Here, ΔP_(sum) satisfies the followingFormula 25.

$\begin{matrix}{{\frac{\sum_{i = 1}^{i - 1}{\Delta \; P_{i}}}{\Delta \; P_{sum}} \times K} < k \leq {\frac{\sum_{i = 1}^{i}{\Delta \; P_{i}}}{\Delta \; P_{sum}} \times K}} & \left( {{Formula}\mspace{14mu} 24} \right) \\{{\Delta \; P_{sum}} = {\sum_{i = 1}^{i_{\max}}{\Delta \; P_{i}}}} & \left( {{Formula}\mspace{14mu} 25} \right)\end{matrix}$

For the motion estimation, a normalized number sequence (test data)having K items made from the trace points is compared with model datanumber sequences with K items each corresponding to one of the multiplemotions. As for the model data number sequence, multiple measurementsare conducted in advance while each of the multiple motions isperformed, and the most frequent direction code among items in thenormalized code string obtained from the multiple motion measurements isset as model data for the item. Since the direction codes are circular,the maximum difference is 4. To calculate the difference between adirection code number and actual one, the following Formula 26 is used.Here, when δC_(i)>4, the following Formula 27 is used.

δC _(i) =|C _(test,1) −C _(model,i)|  (Formula 26)

δC _(i)=8−δC _(i)  (Formula 27)

Here, C_(test,i) is the element at the i-th item of the test datastring, and C_(model,i) is the element at the i-th item of the modeldata string. Next, the sum of squares of the direction code differencebetween the test data and the model data is calculated as a deviationexpressed by the following Formula 28. For example, as illustrated inFIG. 10, the test data and the model data are compared with each otherto calculate the deviation. FIG. 10 is a graph illustrating test dataobtained from measurement and model data which are model codes.

E=δC ₁ ² +δC ₂ ² + . . . +δC _(K) ²  (Formula 28)

Then, the motion corresponding to the model data string of which thedeviation is the smallest is outputted as a recognition result.

Next, operation of sensor 10 according to the embodiment will bedescribed using a flowchart.

FIG. 11 is a flowchart illustrating an example of operation of thesensor according to the embodiment.

In the sensor 10, the N transmitting antenna elements 21 of thetransmitting antenna 20 transmit N transmission signals to a specifiedarea where the living body 50 can be present, using the N transmittingantenna elements 21 (S11).

The M receiving antenna elements 31 of the receiving antenna 30 receiveN received signals including multiple reflection signals which theliving body 50 generates by reflecting the N transmission signalstransmitted by the transmitting antenna 20 (S12).

The circuit 40 calculates the N×M first matrix the components of whichare complex transfer functions each indicating a propagation propertybetween each of the N transmitting antenna elements 21 and each of the Mreceiving antenna elements 31, from each of the N received signalsreceived by each of the M receiving antenna elements 31 during thespecified period (S13).

The circuit 40 extracts the second matrix corresponding to the specifiedfrequency range in the first matrix. The extracted second matrixcorresponds to components affected by vital activities including atleast one of respiration, heartbeats, and body motion of the living body50 (S14).

Using the second matrix, the circuit 40 estimates the three-dimensionalposition at which the living body 50 is present, relative to the sensor10 (S15).

The circuit 40 calculates distance r1 indicating the distance betweenthe living body 50 and the transmitting antenna 20 and distance r2indicating the distance between the living body 50 and the receivingantenna 30, based on the estimated three-dimensional position, theposition of the transmitting antenna 20, and the position of thereceiving antenna 30 (S16).

The circuit 40 calculates the RCS value of the living body 50, using thefirst distance and the second distance (S17).

The circuit 40 estimates the motion of the living body 50, using thecalculated RCS value and the information 42 stored in the memory 41indicating correspondence between the RCS value and the motion of theliving body 50 (S18).

Next, the estimation processing for estimating the motion of the livingbody 50 will be described in detail.

FIG. 12 is a flowchart illustrating an example of details of theestimation processing.

The circuit 40 extracts a period in which the temporal change of thevertical position of the estimated three-dimensional position or thecalculated RCS value is larger than a predetermined value, as a motionperiod in which the living body 50 is in motion (S21).

The circuit 40 converts the temporal changes of the vertical positionobtained from the estimated three-dimensional position and thecalculated RCS value during the extracted motion period into a directionvector, using a predetermined method, and normalizes the directionvector obtained from the conversion to calculate the direction code(S22).

The circuit 40 compares the time series data of the calculated directioncodes with the correspondence information 42 stored in the memory andidentifies the motion associated with the time series data in thecorrespondence information 42 to estimates the motion of the living body50 (S23).

Next, prior learning operation of the sensor 10 for acquiring thecorrespondence information 42 will be described.

FIG. 13 is a flowchart illustrating an example of operation of thesensor in the prior learning processing.

The circuit 40 receives input for specifying a specific motion withnon-illustrated input means (S31). This allows the circuit 40 torecognize that the motion performed during a predetermined period is themotion indicated by the received input.

Next, the same processing as in steps S11 to S17 and steps S21 and S22of the operation of the sensor 10 described above is executedsequentially.

Next, the circuit 40 stores the correspondence information obtained byassociating the motion indicated by the input received at step S31 withthe time series data of the calculated direction codes, as teacher datain the memory 41 (S32).

The sensor 10 according to the embodiment is capable of estimating theposition where the living body 50 is present and the motion of theliving body at the position in a short time with high accuracy.

The sensor 10 detects a part in motion to detect the existence of theliving body 50. Thus, utilizing this, for example, makes it possible toestimate which one of the motions that a living human is performing, themotions being falling down, sitting on a chair, sitting on a floor,standing up from a chair, standing up from a floor, jumping, and turningdirection. This makes it possible to check the existence of a humaneffectively. In addition, since it is possible to check the existence ofa human without image analysis of an image captured by a camera, theexistence of a human can be checked with the privacy of the humanprotected.

Although the sensor 10 according to one or more aspects of the presentdisclosure has been described based on the embodiment as above, thepresent disclosure is not limited to this embodiment. Unless departingfrom the spirit of the present disclosure, one in which variousmodification occurring to those skilled in the art are applied to thisembodiment and an embodiment made by combining constituents fromdifferent embodiments may be included within the one or more aspects ofthe present disclosure.

Although, in the above embodiment, the correspondence information 42 ofthe sensor 10 is information in which model codes, which the verticalpositions and the RCS values are converted into direction codes, areassociated with motions, the correspondence information 42 is notlimited to this. For example, information in which the temporal changesof the vertical position and the RCS value without any conversion areassociated with motions may be used as correspondence information. Inthis case, the motion estimator 450 of the circuit 40 does not need toinclude the direction code calculator 452.

According to the above embodiment, the circuit 40 of the sensor 10 mayestimate, using the estimated motion, the motion performed next by theliving body 50. In other words, the circuit 40 may estimate the motionof the living body 50 in the second motion period next to the firstmotion period, using the posture of the living body 50 when the firstmotion period ends. For example, when the circuit 40 estimates that theliving body 50 stood up, it is apparent that the living body 50 isalready in a standing position. Hence, judging that the living body 50cannot stand up next, the circuit 40 may exclude, in comparing using thecorrespondence information 42, the motion of standing up from themotions to be compared in the correspondence information 42. This makesit possible to estimate the motion of the living body 50 with highaccuracy.

In the above embodiment, the sensor 10 estimate the motion of the livingbody 50 from the data obtained using microwaves, usage of the sensor 10is not limited to estimating motions.

For example, the circuit 40 of the sensor 10 may determine whether thevariation in the horizontal direction is larger than or equal to apredetermined distance from the time series data of thethree-dimensional position of the living body 50, and if the variationis larger than or equal to the predetermined distance, the circuit 40may estimate that the living body 50 is moving in the horizontaldirection. In this case, if the variation in the horizontal direction islarger than a predetermined threshold, the circuit 40 may estimate thatthe living body 50 is running. If it is smaller than the predeterminedthreshold, the circuit 40 may estimate that the living body 50 iswalking. This method makes it possible to estimate the movement of theliving body 50 in the horizontal direction in a short time with highaccuracy.

In addition, for example, the circuit 40 of the sensor 10 may furtherestimate the height of the living body 50, using the vertical positionincluded in the three-dimensional position when the living body 50 isestimated to be moving in the horizontal direction. Specifically, inthis case, the circuit 40 may estimate the height of the living body 50by multiplying the obtained vertical position by a predeterminedcoefficient. As described above, since, for example, the verticalposition of the abdominal part is obtained as the vertical position ofthe living body 50, the height of the living body 50 can be estimated bymultiplying the vertical position of the abdominal part by a coefficientwithin the rage of 1.5 to 2.0 as a predetermined coefficient, forexample. Note that the height is estimated by multiplication of apredetermined coefficient as above based on the assumption that when theliving body 50 is moving in the horizontal direction, a human who is theliving body 50 is in a standing position.

Thus, for example, if multiple living bodies which can be present in aspecified area are known in advance, and the heights of the multipleliving bodies are different, this method can be utilized to identifywhich living body of the multiple living bodies is present and inmotion. For example, if the specified area is a room in a house, andpeople who are living in the house are limited, this method can beutilized to identify who of the people living in the house the obtainedliving body 50 is, by comparing an obtained vertical position with theheights of the people living in the house.

In addition, the circuit 40 of the sensor 10 may further estimate, forexample, the body size of the living body 50, using the RCS value whenthe living body 50 is estimated to be moving in the horizontaldirection. If multiple living bodies which can be present in a specifiedarea are known in advance, and the body sizes of the multiple livingbodies are different, this method can be utilized, for example, toidentify which living body of the multiple living bodies is present andin motion. In other words, similarly to the case of height, it ispossible to utilize the method to identify who is present.

The present disclosure can be utilized for sensors and methods forestimating the directions and the positions of moving objects usingmicrowaves. In particular, the present disclosure can be utilized formeasurement instruments for measuring the directions and the positionsof moving objects including living bodies and mechanical objects, homeappliances which perform control in accordance with the directions andthe positions of the moving objects, direction estimation methods andposition estimation methods used in monitoring apparatuses or the likefor detecting intrusion of a moving object, and height estimationapparatuses using the Rader Cross Section.

What is claimed is:
 1. A sensor comprising: a transmitting antennaincluding N transmitting antenna elements, each of which transmits atransmission signal to a specified area where a living body can bepresent, N being a natural number of 3 or more; a receiving antennaincluding M receiving antenna elements, each of which receives Nreceived signals including a reflection signal which the living bodygenerates by reflecting part of the N transmission signals transmittedby the N transmitting antenna elements, M being a natural number of 3 ormore; a circuit; and a memory which stores correspondence informationindicating correspondence of a motion of the living body with temporalchanges of a radar cross-section value and a vertical position which isa position in a vertical direction at which the living body is presentrelative to the sensor, wherein at least three transmitting antennaelements of the N transmitting antenna elements are arranged inpositions different in the vertical direction and a horizontaldirection, at least three receiving antenna elements of the M receivingantenna elements are arranged in positions different in the verticaldirection and a horizontal direction, and the circuit calculates an N×Mfirst matrix with components, each of which is a complex transferfunction indicating a propagation property between each of the Ntransmitting antenna elements and each of the M receiving antennaelements, from each of the N received signals received by each of the Mreceiving antenna elements during a specified period, extracts a secondmatrix corresponding to a specified frequency range in the first matrix,the second matrix corresponding to components affected by a vitalactivity including at least one of respiration, heartbeats, and bodymotion of the living body, estimates a three-dimensional position atwhich the living body is present relative to the sensor, using thesecond matrix, the three-dimensional position including the verticalposition, calculates a first distance indicating a distance between theliving body and the transmitting antenna and a second distanceindicating a distance between the living body and the receiving antenna,based on the estimated three-dimensional position, a position of thetransmitting antenna, and a position of the receiving antenna,calculates a radar cross-section value of the living body, using thefirst distance and the second distance, and estimates a motion of theliving body, using temporal changes of the estimated three-dimensionalposition and the calculated radar cross-section value, and thecorrespondence information stored in the memory.
 2. The sensor accordingto claim 1, wherein the motion of the living body associated in thecorrespondence information includes falling down, sitting on a chair,sitting on a floor, standing up from a chair, standing up from a floor,jumping, and turning direction, and the circuit estimates which motionthe living body is performing, out of the falling down, sitting on achair, sitting on a floor, standing up from a chair, standing up from afloor, jumping, and turning direction, using the temporal changes of theestimated three-dimensional position and the calculated radarcross-section value and the correspondence information stored in thememory.
 3. The sensor according to claim 1, wherein in estimating themotion of the living body, the circuit extracts a period in which thetemporal change of the estimated vertical position or the calculatedradar cross-section value is larger than a predetermined value, as amotion period in which the living body is in motion, and estimates themotion of the living body, using the temporal changes of the estimatedthree-dimensional position and the calculated radar cross-section valueduring the extracted motion period, and the correspondence informationstored in the memory.
 4. The sensor according to claim 3, wherein thecircuit extracts the motion period, using time series data obtained froma plurality of the vertical positions or a plurality of the radarcross-section values obtained in time series by removing aninstantaneous noise component using a predetermined filter.
 5. Thesensor according to claim 3, wherein the vertical position and the radarcross-section value which are associated with the motion of the livingbody in the correspondence information are expressed by a direction codewhich is obtained by normalizing a direction vector into which thetemporal changes of the vertical position estimated by the circuit andthe radar cross-section value calculated by the circuit in advance whenthe living body performs one motion as the motion of the living body inthe specified area are converted by using a predetermined method, and inestimating the motion of the living body, the circuit converts thetemporal changes of the vertical position obtained from the estimatedthree-dimensional position and the calculated radar cross-section valueduring the extracted motion period into a direction vector using apredetermined method, calculates a direction code by normalizing thedirection vector obtained from conversion, and estimates the motion ofthe living body using the calculated direction code and thecorresponding information.
 6. The sensor according to claim 1, whereinthe circuit estimates the motion of the living body during a secondmotion period next to a first motion period, using a posture of theliving body at the end of the first motion period.
 7. The sensoraccording to claim 1, wherein when a variation in a horizontal directionof the estimated three-dimensional position is larger than or equal to apredetermined distance, the circuit further estimates that the livingbody is moving in the horizontal direction.
 8. The sensor according toclaim 7, wherein the circuit further estimates a height of the livingbody, using the vertical position included in the three-dimensionalposition from which the living body is estimated to be moving in thehorizontal direction.
 9. The sensor according to claim 7, wherein thecircuit further estimates a body size of the living body, using theradar cross-section value calculated when the living body is estimatedto be moving in the horizontal direction.
 10. The sensor according toclaim 1, wherein the specified period is about half a cycle of at leastone of respiration, heartbeats, and body motion of the living body. 11.A method of estimating a motion of a living body using a sensor, thesensor comprising: a transmitting antenna including N transmittingantenna elements, N being a natural number of 3 or more; a receivingantenna including M receiving antenna elements, M being a natural numberof 3 or more; a circuit; and a memory which stores correspondenceinformation indicating correspondence among a motion of a living body, aradar cross-section value, and a vertical position which is a positionin a vertical direction at which the living body is present relative tothe sensor, wherein at least three transmitting antenna elements of theN transmitting antenna elements are arranged in positions different inthe vertical direction and a horizontal direction, at least threereceiving antenna elements of the M receiving antenna elements arearranged in positions different in the vertical direction and thehorizontal direction, and the method comprises: transmitting Ntransmission signals to a specified area where the living body can bepresent, using the N transmitting antenna elements; receiving, by usingeach of the M receiving antenna elements, N received signals including areflection signal which the living body generates by reflecting part ofthe transmitted N transmission signals; calculating an N×M first matrixwith components, each of which is a complex transfer function indicatinga propagation property between each of the N transmitting antennaelements and each of the M receiving antenna elements, from each of theN received signals received by each of the M receiving antenna elementsduring a specified period; extracting a second matrix corresponding to aspecified frequency range in the first matrix, the second matrixcorresponding to components affected by a vital activity including atleast one of respiration, heartbeats, and body motion of the livingbody; estimating a three-dimensional position at which the living bodyis present relative to the sensor, using the second matrix, thethree-dimensional position including the vertical position; calculatinga first distance indicating a distance between the living body and thetransmitting antenna and a second distance indicating a distance betweenthe living body and the receiving antenna, based on the estimatedthree-dimensional position, a position of the transmitting antenna, anda position of the receiving antenna; calculating a radar cross-sectionvalue of the living body, using the first distance and the seconddistance; and estimating a motion of the living body, using temporalchanges of the estimated three-dimensional position and the calculatedradar cross-section value, and the correspondence information stored inthe memory.